ObjectiveThis study aimed to investigate the incidence of the pulmonary sarcomatoid carcinoma (PSC), to compare the clinical characteristics and overall survival (OS) of patients with PSC and those with other non-small-cell lung cancer (oNSCLC), so as to analyze the factors affecting the OS of patients with PSC and construct a nomogram prediction model.MethodsData of patients with PSC and those with oNSCLC diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database were collected. The age-adjusted incidence of PSC was calculated. The characteristics of patients with PSC and those with oNSCLC were compared, then the patients were matched 1:2 for further survival analysis. Patients with PSC were randomly divided into training set and testing set with a ratio of 7:3. The Cox proportional hazards model was used to identify the covariates associated with the OS. Significant covariates were used to construct the nomogram, and the C-index was calculated to measure the discrimination ability. The accuracy of the nomogram was compared with the tumor–node–metastasis (TNM) clinical stage, and the corresponding area under the curve was achieved.ResultsA total of 1049 patients with PSC were enrolled, the incidence of PSC was slowly decreased from 0.120/100,000 in 2004 to 0.092/100,000 in 2015. Before PSM, 793 PSC patients and 191356 oNSCLC patients were identified, the proportion of male, younger patients (<65 years), grade IV, TNM clinical stage IV was higher in the PSC. The patients with PSC had significantly poorer OS compared with those with oNSCLC. After PSM, PSC still had an extremely inferior prognosis. Age, sex, TNM clinical stage, chemotherapy, radiotherapy, and surgery were independent factors for OS. Next, a nomogram was established based on these factors, and the C-indexs were 0.775 and 0.790 for the training and testing set, respectively. Moreover, the nomogram model indicated a more comprehensive and accurate prediction than the TNM clinical stage.ConclusionsThe incidence of PSC was slowly decreased. PSC had a significantly poor prognosis compared with oNSCLC. The nomogram constructed in this study accurately predicted the prognosis of PSC, performed better than the TNM clinical stage.

Background The study was conducted to compare the clinicopathological characteristics, survival outcomes, and metastatic patterns between pulmonary large cell neuroendocrine carcinoma (LCNEC) and other non‐small cell lung cancer (ONSCLC), and to identify the prognostic factors of LCNEC. Methods Data of patients diagnosed with LCNEC and ONSCLC from 2004 to 2014 were obtained from the Surveillance, Epidemiology, and End Results dataset. Pearson’s chi‐square tests were used to compare differences in clinicopathological characteristics. The Kaplan–Meier method was used for survival analysis. A propensity score was used for matching and a Cox proportional hazards model was used for multivariate and subgroup analyses. Results A total of 2368 LCNEC cases and 231 672 ONSCLC cases were identified. LCNEC incidence increased slightly over time. Except for marital status, LCNEC patients had obviously different biological features to ONSCLC patients. Survival analysis showed that LCNEC had poorer outcomes than ONSCLC. Multivariate analysis revealed that female gender, black race, surgery, radiation, and chemotherapy were protective factors for LCNEC. Matched subgroup analysis further demonstrated that most subgroup factors favored ONSCLC, especially in early stage. Early‐stage LCNEC patients had a higher risk of lung cancer‐specific death than early‐stage ONSCLC patients. Moreover, metastatic patterns were different between LCNEC and ONSCLC. LCNEC patients with isolated liver metastasis or combined invasion to other organs had poorer survival rates. Conclusions LCNEC has totally different clinicopathological characteristics and metastatic patterns to ONSCLC. LCNEC also has poorer survival outcomes, primarily because of isolated liver metastasis or combined invasion to other organs. Most subgroup factors are adverse factors for LCNEC.

Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.